Background: One of the major contributors to disability in Knee osteoarthritis (KOA) patients is weakness in the Quadriceps Femoris muscle. Neuromuscular electrical stimulation (NMES) has been used in rehabilitation for patients suffering from muscle weakness. Thus, the purpose of the study was to assess the effectiveness of NMES and exercise therapy, for improving pain, muscle weakness and function among patients with KOA. Methods: A randomized controlled trial was conducted with 75 female patients diagnosed with KOA. Participants were divided into three intervention groups: NMES-only, exercise therapy (Exs) alone, and a combination of NMES and exercise (NMES + Exs). All patients underwent 12 supervised treatment sessions, three times a week. Outcome measures included pain intensity measured by visual analog scale (VAS), knee flexion range of motion (FROM), thigh muscle girth (TG), thickness of the Vastus Medialis Oblique (VMO), timed up and go test (TUG), six-minute walk test (6MWT), and WOMAC scores. Statistical analyses (ANOVA and Kruskal–Wallis) methods were done to compare the amounts at the baseline, immediately after treatment and after 12 weeks. Results: The NMES group exhibited a significant reduction in pain at the 12-week follow-up compared to the other groups(p = 0.022). The NMES + Exs group showed better outcomes in terms of FROM, TG, and VMO thickness post-intervention (p < 0.0001, p < 0.004, p = 0.003, respectively) and at the 12-week follow-up (p < 0.0001, p < 0.0001, p < 0.0001, respectively). Additionally, NMES was superior in improving TUG and 6MWT post-intervention (p < 0.0001, p = 0.038, respectively) and during the follow-up assessments (p < 0.0001, p = 0.029, respectively). The NMES + Exs group achieved better WOMAC stiffness scores at both post-intervention and follow-up evaluations (p < 0.0001, p < 0.0001, respectively). Furthermore, at the 12-week follow-up, NMES + Exs group outperformed the others in WOMAC pain and function subscales (p = 0.003, p = 0.017, respectively), while the NMES group demonstrated better WOMAC total scores compared to the other groups (p = 0.007). Conclusion: The combination of NMES and exercise seems to be an efficient approach for managing KOA, as it enhances knee flexion range and TG, increases VMO thickness, and improves WOMAC scores. On the other hand, NMES alone was found to be effective in improving the physical function of KOA patients. Trial registration: IRCT20101228005486N7 (06–02-2020).

Cohort studies are a core aspect of clinical research which helps to gather a large volume of data over time. As digital technologies evolve, managing these data has become increasingly complex. Therefore, the use of cohort data management systems (CDMS) has been suggested to enhance data accuracy, confidentiality, and consistency. However, the functional and non-functional requirements of these systems have not been adequately emphasized in literature. This study aimed to identify the key functional and non-functional requirements of these systems. This was a scoping review conducted in 2025, and articles were searched in PubMed, Scopus, Web of Science, ProQuest, IEEE Xplore, and the Cochrane Library databases as well as Google Scholar. Initially, 843 articles were retrieved, and finally, 45 articles published between 1st January 2005 and 31st June 2025 were selected. Nine functional and eight non-functional requirements were identified for CDMS. These systems are essential for facilitating cohort studies through data management, data processing and analysis. Advanced tools like AI, visual dashboards, and automation have improved CDMS functionalities. The most important non-functional requirements included flexibility, security and usability. CDMS must support comprehensive data operations, secure access, user engagement, and interoperability while ensuring scalability, privacy, and regulatory compliance. Requirements such as maintainability, although less emphasized, are essential for the long-term development and optimization of data management systems. Future research should focus on emerging technologies like blockchain and Internet of Things (IoT) to enhance the security, integrity, and performance of CDMS.

Introduction: Knee osteoarthritis (KOA) stands out as the most common synovial joint disease among people, leading to a reduced quality of life, persistent pain, muscle weakness, and significant functional impairments. The aim of this study is to carry out an investigation that assesses the efficacy of clinic-supervised and internet-based exercise therapy (IET) on pain, range of knee flexion, and physical function of patients with KOA. Methods: This randomized controlled trial enrolled 54 patients between the ages of 50 and 75, all of whom had confirmed KOA. These patients were randomly allocated to one of two groups, both of which underwent 18 sessions of exercise therapy. One group received supervised therapy, while the other received therapy delivered via the internet. The study measured various outcomes, including pain intensity assessed with the VAS, thigh girth (TG), knee active flexion range of motion (FROM), the six-minute walk test (6MWT), the timed up and go test (TUG), and knee functionality evaluated using the Western Ontario and McMaster Universities Osteoarthritis (WOMAC) questionnaire, both before and after the intervention. We used the paired t-test for intra-group analysis and the independent samples t-test and analysis of covariance (ANCOVA) test to compare differences between the groups. Results: Significant differences in outcomes were observed before and after the intervention in both groups (P=0.001). However, the ANCOVA test revealed no significant differences between the groups following the study. The statistical analysis, performed with an independent-samples t-test, indicated no significant differences between the two groups concerning VAS, active knee flexion, thigh girth, and WOMAC scores, suggesting that both supervised exercise therapy (SET) and IET protocols yielded similar effectiveness. Nevertheless, when employing the independent-samples t-test, there were significant differences between the groups in the TUG (P=0.001) and the 6MWT (P=0.017), with the IET group demonstrating superior performance. Conclusion: The results of this study indicate that both SET and IET produce comparable outcomes in terms of reducing pain, increasing thigh girth, increasing knee flexion range, and improving WOMAC scores and the physical function of patients with KOA. The internet-based approach may offer added convenience and motivation for patients.

The COVID-19 pandemic highlighted the need to understand factors influencing individuals' risk perceptions and health behaviors. This study aimed to explore the roles of individuals' knowledge, perception, and health-related issues in determining COVID-19 risk by developing a predictive model for classifying individuals into the risk categories, incorporating both clustering and model interpretation techniques. To identify distinct COVID-19 risk groups, clustering analysis was applied using the demographic, health, and behavioral data. Subsequently, several machine learning models-including CatBoost, XGBoost, Random Forest, Generalized Linear Model (GLM), Decision Tree, H2O Deep Neural Network (DNN), and L2 SVM-were used to predict risk classifications. SHAP (SHapley Additive exPlanations) analysis was applied to interpret the contribution of individual features in model predictions. Three distinct risk classes were identified: Class 0 (high knowledge, low-risk factors, no household COVID-19 diagnosis), Class 1 (health-related issues (e.g., hypertension), low lnowldge), and Class 2 (high knowledge, higher health risks (e.g., hypertension, household COVID-19 diagnosis)). L2 SVM achieved the highest accuracy (0.9724), followed by XGBoost (0.9301) and CatBoost (0.9265). SHAP analysis revealed that household hygiene practices and health-related issues, such as hypertension and Gastrointestinal symptoms were key drivers of risk classification. Integrating individuals' knowledge, perception, and health-related issues into COVID-19 risk assessments enhances predictive accuracy. Public health policies should focus on both physical and psychological factors to effectively mitigate the spread and impact of COVID-19. Data-driven models may inform future efforts to prioritize resource allocation and improve public health responses for vulnerable populations.

Introduction: Computer-based simulation technologies, including virtual reality (VR), augmented reality (AR), mixed reality (MR), and three-dimensional (3D) modeling, are increasingly used in pediatric cardiovascular care. Despite growing adoption of these technologies, no comprehensive synthesis across VR, AR, MR, and 3D modeling modalities exists for pediatric cardiovascular care. Objective: This review aimed to synthesize evidence regarding the applications and outcomes of computer-based simulation technologies in pediatric cardiovascular diseases. Methods: A systematic review was conducted by searching nine databases (PubMed, Web of Science, Scopus, Ovid, the Cochrane Library, IEEE Xplore, ProQuest, CINAHL, and EBSCO Host) for eligible studies published up to 1 September 2025. Thirty-six studies were included and appraised using the Mixed Methods Appraisal Tool (MMAT), the Critical Appraisal Skills Programme (CASP) checklist, and the ROBINS-I tool. Substantial clinical and methodological heterogeneity precluded meta-analysis. Results: VR was the most frequently used modality (n = 14), followed by 3D modeling (n = 8), multimodal approaches (n = 8), MR (n = 6), and AR (n = 3). This technology was mainly used for congenital heart defects (n = 18). MR holography improved diagnostic accuracy for complex anomalies (95.5% vs. 89.7%); VR-based surgical plans aligned better with the real surgery than did 2D imaging plans (80% vs. 66%); VR was preferred over 3D-printed models by 87% of participants (8.5/10 vs. 6.3/10 for anatomical understanding); and VR curricula (Stanford Virtual Heart) significantly increased CHD knowledge scores among students and residents (p < 0.05). However, this evidence was predominantly derived from small, single-center observational studies, with barriers including hardware limitations and limited long-term outcome data. Conclusions: Computer-based simulation technologies show considerable potential for surgical planning, diagnostic assessment, and education in pediatric cardiovascular diseases. They can be used as complementary tools rather than replacements for standard imaging or clinical judgment. While VR and 3D modeling show promise for surgical planning and education, multicenter comparative studies with standardized outcomes and cost-effectiveness analyses are essential before routine clinical implementation.